An ontology of slums for image-based classification

Kohli, Divyani; Sliuzas, Richard; Kerle, N.; Stein, Alfred

doi:10.1016/j.compenvurbsys.2011.11.001

Cited by 251 publications

(258 citation statements)

References 35 publications

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“…Based on the four morphological dimensions of deprived areas in Mumbai, i.e., environment, density, geometry, and texture pattern (building on the earlier work of [4,15,22,25,[55][56][57]), image features are created with the potential to capture the diversity of such areas (Figure 2). This list of image features (Figure 7) is generated based on distinguishing features reported in slum mapping studies (e.g., [4,10,22,23,56,[58][59][60]), as well as by considering the local characteristics of deprived areas in Mumbai.…”

Section: Extraction Of Features To Map the Diversity Of Deprivationmentioning

confidence: 99%

“…Earlier studies [15,25] showed that deprived areas display diversity in terms of environmental (environ) features such as location on steep slope. Furthermore, land cover/use characteristics often vary among deprived areas; e.g., large and very densely built-up areas have little land cover/use heterogeneity (measured e.g., by SHDI and SHEI) while small slum pockets are often surrounded by vegetation or other land cover/use types.…”

Section: Extraction Of Features To Map the Diversity Of Deprivationmentioning

confidence: 99%

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Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai

et al. 2017

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Many cities in the Global South are facing rapid population and slum growth, but lack detailed information to target these issues. Frequently, municipal datasets on such areas do not keep up with such dynamics, with data that are incomplete, inconsistent, and outdated. Aggregated census-based statistics refer to large and heterogeneous areas, hiding internal spatial differences. In recent years, several remote sensing studies developed methods for mapping slums; however, few studies focused on their diversity. To address this shortcoming, this study analyzes the capacity of very high resolution (VHR) imagery and image processing methods to map locally specific types of deprived areas, applied to the city of Mumbai, India. We analyze spatial, spectral, and textural characteristics of deprived areas, using a WorldView-2 imagery combined with auxiliary spatial data, a random forest classifier, and logistic regression modeling. In addition, image segmentation is used to aggregate results to homogenous urban patches (HUPs). The resulting typology of deprived areas obtains a classification accuracy of 79% for four deprived types and one formal built-up class. The research successfully demonstrates how image-based proxies from VHR imagery can help extract spatial information on the diversity and cross-boundary clusters of deprivation to inform strategic urban management.

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Section: Extraction Of Features To Map the Diversity Of Deprivationmentioning

confidence: 99%

Section: Extraction Of Features To Map the Diversity Of Deprivationmentioning

confidence: 99%

Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai

et al. 2017

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“…Thus, it is mostly semi-automatic, involving a human expert combined with statistical, syntactic and semantic techniques (Makki et al, 2008). Kohli et al (2012) manually extracted information using literature reviews, interviews and surveys from experts to develop the generic slum ontology (GSO), which comprises of concept that refer to the morphology of the environment. EmotionsOnto, a generic ontology used to detect and describe emotional expressions, manually acquires information from users (Gil et al, 2015).…”

Section: Generic Ontologiesmentioning

confidence: 99%

Towards knowledge modeling and manipulation technologies: A survey

Bimba

Al-Hunaiyyan

Mahmud

et al. 2016

International Journal of Information Management

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A system which represents knowledge is normally referred to as a knowledge based system (KBS). This article focuses on surveying publications related to knowledge base modelling and manipulation technologies, between the years 2000-2015. A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey. The main aim of this study is to identify different knowledge base modelling and manipulation techniques based on 4 categories; 1) linguistic knowledge base; 2) expert knowledge base; 3) ontology and 4) cognitive knowledge base. This led to the proposition of 8 research questions, which focused on the different categories of knowledge base modelling technologies, their underlying theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages. A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge. A promising technique for knowledge-based business management and other knowledge related applications is also discussed.

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“…A Figura 4, extraída do artigo de Kohli et al (2012), exemplifica as divergências gráficas na delimitação, elaboradas por cinco especialistas no assunto. Ainda que haja uma significativa área comum entre os analistas, há evidentes incompatibilidades nos limites de um assentamento precário de Kisumu, no Quênia.…”

Section: Dimensão E Representação Dos Assentamentosunclassified

As definições de assentamentos precários e favelas e suas implicações nos dados populacionais: abordagem da análise de conteúdo

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2015

urbe, Rev. Bras. Gest. Urbana

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ResumoO objetivo deste artigo é discutir as diferentes definições de assentamentos precários e favelas e suas implicações nos dados populacionais. A amostra de definições foi extraída de pesquisas relacionadas ao United Nations Human Settlements Programme (UN-Habitat) e ao Instituto Brasileiro de Geografia e Estatística (IBGE). A manipulação dos dados foi realizada conforme a abordagem da análise de conteúdo (AC). A quantificação, realizada com o software Iramuteq, se baseou na frequência de palavras e na análise fatorial de correspondências (AFC). As análises qualitativas e quantitativas dessas definições ressaltaram duas principais diferenças: na caracterização do objeto (área, edificação e ambas); e nos tipos de qualificação (aspectos legais, padrão construtivo, deficiência de infraestrutura, propriedade da terra, densidade populacional, referências geográficas e tipificação dos moradores). Como o número de qualificações é elevado e seu conteúdo diverso, os dados populacionais agregam informações muito diferentes, tornando sua comparação menos precisa. Em decorrência, essa imprecisão tende a se ampliar em função do crescimento da extensão territorial e do número de países analisados.

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An ontology of slums for image-based classification

Cited by 251 publications

References 35 publications

Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai

Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai

Towards knowledge modeling and manipulation technologies: A survey

As definições de assentamentos precários e favelas e suas implicações nos dados populacionais: abordagem da análise de conteúdo

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